The evaluation of generative models has received significant attention in the machine learning community.

However, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the strongest andmost popular Graph Neural Networks (GNNs).

We identify three key challenges when using learned dual variables in a primal-dual algorithm.

Images produced bydiffusionmodels areincreasingly popular indigital artwork and visual marketing. However, such generated images might replicate content from existing ones andpose thechallenge ofcontent originality.

Techniques have recently shifted to neural mesh autoencoders.